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!pip install hyperactive
# Re-install pandas 2.2
!pip install --upgrade pandas
!pip install hyperactive
# Re-install pandas 2.2
!pip install --upgrade pandas
Sample strategy¶
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# import talib.abstract as ta
from lettrade import DataFeed, Strategy, indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import inject_indicators
inject_indicators()
class SmaCross(Strategy):
ema1_period = 9
ema2_period = 21
def indicators(self, df: DataFeed):
# df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period)
# df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period)
df["ema1"] = df.close.ema(window=self.ema1_period)
df["ema2"] = df.close.ema(window=self.ema2_period)
df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)
def next(self, df: DataFeed):
if len(self.orders) > 0 or len(self.positionsons) > 0:
return
if df.l.signal_ema_crossover[-1]:
price = df.l.close[-1]
self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001)
elif df.l.signal_ema_crossunder[-1]:
price = df.l.close[-1]
self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)
lt = let_backtest(
strategy=SmaCross,
datas="example/data/data/EURUSD_5m_0_10000.csv",
account=ForexBackTestAccount,
# plotter=None,
)
# import talib.abstract as ta
from lettrade import DataFeed, Strategy, indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import inject_indicators
inject_indicators()
class SmaCross(Strategy):
ema1_period = 9
ema2_period = 21
def indicators(self, df: DataFeed):
# df["ema1"] = ta.EMA(df, timeperiod=self.ema1_period)
# df["ema2"] = ta.EMA(df, timeperiod=self.ema2_period)
df["ema1"] = df.close.ema(window=self.ema1_period)
df["ema2"] = df.close.ema(window=self.ema2_period)
df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)
def next(self, df: DataFeed):
if len(self.orders) > 0 or len(self.positionsons) > 0:
return
if df.l.signal_ema_crossover[-1]:
price = df.l.close[-1]
self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001)
elif df.l.signal_ema_crossunder[-1]:
price = df.l.close[-1]
self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)
lt = let_backtest(
strategy=SmaCross,
datas="example/data/data/EURUSD_5m_0_10000.csv",
account=ForexBackTestAccount,
# plotter=None,
)
Example¶
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from hyperactive import Hyperactive
from hyperactive.optimizers import HillClimbingOptimizer
# define the model in a function
def params_parser(args):
return {
"ema1_period": args["ema1_period"],
"ema2_period": args["ema2_period"],
}
def result_parser(result):
return result["equity"]
model = lt.optimize_model(
params_parser=params_parser,
result_parser=result_parser,
)
# search space determines the ranges of parameters you want the optimizer to search through
search_space = {
"ema1_period": list(range(5, 25, 1)),
"ema2_period": list(range(10, 50)),
}
optimizer = HillClimbingOptimizer(epsilon=0.1, distribution="laplace", n_neighbours=4)
# start the optimization run
hyper = Hyperactive()
hyper.add_search(model, search_space, optimizer=optimizer, n_iter=1000)
hyper.run()
from hyperactive import Hyperactive
from hyperactive.optimizers import HillClimbingOptimizer
# define the model in a function
def params_parser(args):
return {
"ema1_period": args["ema1_period"],
"ema2_period": args["ema2_period"],
}
def result_parser(result):
return result["equity"]
model = lt.optimize_model(
params_parser=params_parser,
result_parser=result_parser,
)
# search space determines the ranges of parameters you want the optimizer to search through
search_space = {
"ema1_period": list(range(5, 25, 1)),
"ema2_period": list(range(10, 50)),
}
optimizer = HillClimbingOptimizer(epsilon=0.1, distribution="laplace", n_neighbours=4)
# start the optimization run
hyper = Hyperactive()
hyper.add_search(model, search_space, optimizer=optimizer, n_iter=1000)
hyper.run()
[0] _optimize_model (Hill Climbing): 100%|──────────| 1000/1000 [00:01<00:00, 758.47it/s, best_iter=0, best_pos=[9 0], best_score=1183.88]
Results: '_optimize_model'
Best score: 1183.88
Best parameter set:
'ema1_period' : 14.0
'ema2_period' : 10.0
Best iteration: 0
Random seed: 1082602856
Evaluation time : 0.4881856441497803 sec [83.53 %]
Optimization time : 0.09622955322265625 sec [16.47 %]
Iteration time : 0.5844151973724365 sec [1711.11 iter/sec]
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df = hyper.search_data(model)
df
df = hyper.search_data(model)
df
Out[3]:
| ema1_period | ema2_period | score | |
|---|---|---|---|
| 0 | 9 | 29 | 850.48 |
| 1 | 23 | 27 | 971.48 |
| 2 | 11 | 23 | 851.68 |
| 3 | 11 | 36 | 940.68 |
| 4 | 17 | 23 | 959.68 |
| ... | ... | ... | ... |
| 995 | 11 | 24 | 831.78 |
| 996 | 8 | 20 | 879.48 |
| 997 | 21 | 11 | 1096.68 |
| 998 | 8 | 35 | 890.58 |
| 999 | 15 | 14 | 1007.28 |
1000 rows × 3 columns
Plot¶
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lt.plotter.heatmap()
lt.plotter.heatmap()
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lt.plotter.contour(x="ema1_period", y="ema2_period", z="equity")
lt.plotter.contour(x="ema1_period", y="ema2_period", z="equity")
Plot plotly¶
Init Plotly environment¶
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import plotly.io as pio
pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"
import plotly.io as pio
pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"
Type 1¶
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import plotly.express as px
fig = px.density_heatmap(
df,
x="ema1_period",
y="ema2_period",
z="score",
nbinsx=20,
nbinsy=40,
histfunc="max",
color_continuous_scale="Viridis",
)
fig.show()
import plotly.express as px
fig = px.density_heatmap(
df,
x="ema1_period",
y="ema2_period",
z="score",
nbinsx=20,
nbinsy=40,
histfunc="max",
color_continuous_scale="Viridis",
)
fig.show()
Type 2¶
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import plotly.express as px
fig = px.density_contour(
df,
x="ema1_period",
y="ema2_period",
z="score",
histfunc="max",
)
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()
import plotly.express as px
fig = px.density_contour(
df,
x="ema1_period",
y="ema2_period",
z="score",
histfunc="max",
)
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()